{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:27:54Z","timestamp":1774625274425,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005672","name":"Funda\u00e7\u00e3o de Apoio ao Desenvolvimento do Ensino, Ci\u00eancia e Tecnologia do Estado de Mato Grosso do Sul","doi-asserted-by":"publisher","award":["71\/022.356\/2021"],"award-info":[{"award-number":["71\/022.356\/2021"]}],"id":[{"id":"10.13039\/501100005672","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005672","name":"Funda\u00e7\u00e3o de Apoio ao Desenvolvimento do Ensino, Ci\u00eancia e Tecnologia do Estado de Mato Grosso do Sul","doi-asserted-by":"publisher","award":["310596\/2022-0"],"award-info":[{"award-number":["310596\/2022-0"]}],"id":[{"id":"10.13039\/501100005672","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council for Scientific and Technological Development","doi-asserted-by":"publisher","award":["71\/022.356\/2021"],"award-info":[{"award-number":["71\/022.356\/2021"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council for Scientific and Technological Development","doi-asserted-by":"publisher","award":["310596\/2022-0"],"award-info":[{"award-number":["310596\/2022-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>The combination of Bidirecional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNNs) has been extensively explored in the text classification literature, usually employing BERT as a feature extractor combined with heterogeneous static graphs. BERT transfers information via token embeddings, which are propagated through GNNs. Text-specific information defines a static heterogeneous graph. Static graphs represent specific relationships and do not have the flexibility to add new knowledge to the graph. To address this issue, we build a tied connection between BERT and GNN exclusively using token embeddings to define the graph and propagate the embeddings, which can force the BERT to redefine the GNN graph topology to improve accuracy. Thus, in this study, we re-examine the design spaces and test the limits of what this pure homogeneous graph using BERT embeddings can achieve. Homogeneous graphs offer structural simplicity and greater generalization capabilities, particularly when integrated with robust representations like those provided by BERT. To improve accuracy, the proposed approach also incorporates text augmentation and label propagation at test time. Experimental results show that the proposed method outperforms state-of-the-art methods across all datasets analyzed, with consistent accuracy improvements as more labeled examples are included.<\/jats:p>","DOI":"10.3390\/informatics12010020","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T07:48:22Z","timestamp":1739778502000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DynGraph-BERT: Combining BERT and GNN Using Dynamic Graphs for Inductive Semi-Supervised Text Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4246-2126","authenticated-orcid":false,"given":"Eliton Luiz Scardin","family":"Perin","sequence":"first","affiliation":[{"name":"Faculty of Computing (FACOM), Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1746-8414","authenticated-orcid":false,"given":"Mariana Caravanti de","family":"Souza","sequence":"additional","affiliation":[{"name":"Faculty of Computing (FACOM), Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8274-2707","authenticated-orcid":false,"given":"Jonathan de Andrade","family":"Silva","sequence":"additional","affiliation":[{"name":"Faculty of Computing (FACOM), Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4471-0886","authenticated-orcid":false,"given":"Edson Takashi","family":"Matsubara","sequence":"additional","affiliation":[{"name":"Faculty of Computing (FACOM), Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14911","DOI":"10.1007\/s00521-023-08494-0","article-title":"Text classification on heterogeneous information network via enhanced GCN and knowledge","volume":"35","author":"Li","year":"2023","journal-title":"Neural Comput. 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